OpenRaiser/Intern-Atlas huggingface dataset:
A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection
Venue: arXiv cs.CV 2020
Year: 2020

Object detection in camera images, using deep learning has been proven successfully in recent years. Rising detection rates and computationally efficient network structures are pushing this technique towards application in production vehicles. Nevertheless, the sensor quality of the camera is limited in severe weather conditions and through increased sensor noise in sparsely lit areas and at night. Our approach enhances current 2D object detection networks by fusing camera data and projected sparse radar data in the network layers. The proposed CameraRadarFusionNet (CRF-Net) automatically learns at which level the fusion of the sensor data is most beneficial for the detection result. Additionally, we introduce BlackIn, a training strategy inspired by Dropout, which focuses the learning on a specific sensor type. We show that the fusion network is able to outperform a state-of-the-art image-only network for two different datasets. The code for this research will be made available to the public at: https://github.com/TUMFTM/CameraRadarFusionNet.
Excursion Search for Constrained Bayesian Optimization under a Limited Budget of Failures
Venue: arXiv cs.LG 2020
Year: 2020

When learning to ride a bike, a child falls down a number of times before achieving the first success. As falling down usually has only mild consequences, it can be seen as a tolerable failure in exchange for a faster learning process, as it provides rich information about an undesired behavior. In the context of Bayesian optimization under unknown constraints (BOC), typical strategies for safe learning explore conservatively and avoid failures by all means. On the other side of the spectrum, non conservative BOC algorithms that allow failing may fail an unbounded number of times before reaching the optimum. In this work, we propose a novel decision maker grounded in control theory that controls the amount of risk we allow in the search as a function of a given budget of failures. Empirical validation shows that our algorithm uses the failures budget more efficiently in a variety of optimization experiments, and generally achieves lower regret, than state-of-the-art methods. In addition, we propose an original algorithm for unconstrained Bayesian optimization inspired by the notion of excursion sets in stochastic processes, upon which the failures-aware algorithm is built.
kiwiPy: Robust, high-volume, messaging for big-data and computational science workflows
Venue: arXiv cs.DC 2020
Year: 2020

In this work we present kiwiPy, a Python library designed to support robust message based communication for high-throughput, big-data, applications while being general enough to be useful wherever high-volumes of messages need to be communicated in a predictable manner. KiwiPy relies on the RabbitMQ protocol, an industry standard message broker, while providing a simple and intuitive interface that can be used in both multithreaded and coroutine based applications. To demonstrate some of kiwiPy's functionality we give examples from AiiDA, a high-throughput simulation platform, where kiwiPy is used as a key component of the workflow engine.
Minimizing the Installation Cost of Ground Stations in Satellite Networks: Complexity, Dynamic Programming and Approximation Algorithm
Venue: arXiv cs.NI 2020
Year: 2020

In this letter, we study the optimum selection of ground stations (GSs) in RF/optical satellite networks (SatNets) in order to minimize the overall installation cost under an outage probability requirement, assuming independent weather conditions between sites. First, we show that the optimization problem can be formulated as a binary-linear-programming problem, and then we give a formal proof of its NP-hardness. Furthermore, we design a dynamic-programming algorithm of pseudo-polynomial complexity with global optimization guarantee as well as an efficient (polynomial-time) approximation algorithm with provable performance guarantee on the distance of the achieved objective value from the global optimum. Finally, the performance of the proposed algorithms is verified through numerical simulations.
Online path sampling control with progressive spatio-temporal filtering
Venue: arXiv cs.GR 2020
Year: 2020

This work introduces progressive spatio-temporal filtering, an efficient method to build all-frequency approximations to the light transport distribution into a scene by filtering individual samples produced by an underlying path sampler, using online, iterative algorithms and data-structures that exploit both the spatial and temporal coherence of the approximated light field. Unlike previous approaches, the proposed method is both more efficient, due to its use of an iterative temporal feedback loop that massively improves convergence to a noise-free approximant, and more flexible, due to its introduction of a spatio-directional hashing representation that allows to encode directional variations like those due to glossy reflections. We then introduce four different methods to employ the resulting approximations to control the underlying path sampler and/or modify its associated estimator, greatly reducing its variance and enhancing its robustness to complex lighting scenarios. The core algorithms are highly scalable and low-overhead, requiring only minor modifications to an existing path tracer.
Participatory Problem Formulation for Fairer Machine Learning Through Community Based System Dynamics
Venue: arXiv cs.CY 2020
Year: 2020

Recent research on algorithmic fairness has highlighted that the problem formulation phase of ML system development can be a key source of bias that has significant downstream impacts on ML system fairness outcomes. However, very little attention has been paid to methods for improving the fairness efficacy of this critical phase of ML system development. Current practice neither accounts for the dynamic complexity of high-stakes domains nor incorporates the perspectives of vulnerable stakeholders. In this paper we introduce community based system dynamics (CBSD) as an approach to enable the participation of typically excluded stakeholders in the problem formulation phase of the ML system development process and facilitate the deep problem understanding required to mitigate bias during this crucial stage.
IoT Based Traffic Management System for Ambulances
Venue: arXiv cs.DC 2020
Year: 2020

Lack of efficient traffic control can lead to the loss of thousands of lives due to ambulance not being able to reach the hospital in time. Also, with the current annual growth of Vehicles being around 11% while the annual road extension remaining around 4% in developing countries such as India, the problem is further worsening. So, to deal with this problem the paper presents a novel, easy toimplement alternative for traffic management during emergency situations requiring only three main devices: Arduino UNO, GPS neo 6M and SIM 900A.
Neural Entity Linking on Technical Service Tickets
Venue: arXiv cs.CL 2020
Year: 2020

Entity linking, the task of mapping textual mentions to known entities, has recently been tackled using contextualized neural networks. We address the question whether these results -- reported for large, high-quality datasets such as Wikipedia -- transfer to practical business use cases, where labels are scarce, text is low-quality, and terminology is highly domain-specific. Using an entity linking model based on BERT, a popular transformer network in natural language processing, we show that a neural approach outperforms and complements hand-coded heuristics, with improvements of about 20% top-1 accuracy. Also, the benefits of transfer learning on a large corpus are demonstrated, while fine-tuning proves difficult. Finally, we compare different BERT-based architectures and show that a simple sentence-wise encoding (Bi-Encoder) offers a fast yet efficient search in practice.
Beyond MeSH: Fine-Grained Semantic Indexing of Biomedical Literature based on Weak Supervision
Venue: arXiv cs.IR 2020
Year: 2020

In this work, we propose a method for the automated refinement of subject annotations in biomedical literature at the level of concepts. Semantic indexing and search of biomedical articles in MEDLINE/PubMed are based on semantic subject annotations with MeSH descriptors that may correspond to several related but distinct biomedical concepts. Such semantic annotations do not adhere to the level of detail available in the domain knowledge and may not be sufficient to fulfil the information needs of experts in the domain. To this end, we propose a new method that uses weak supervision to train a concept annotator on the literature available for a particular disease. We test this method on the MeSH descriptors for two diseases: Alzheimer's Disease and Duchenne Muscular Dystrophy. The results indicate that concept-occurrence is a strong heuristic for automated subject annotation refinement and its use as weak supervision can lead to improved concept-level annotations. The fine-grained semantic annotations can enable more precise literature retrieval, sustain the semantic integration of subject annotations with other domain resources and ease the maintenance of consistent subject annotations, as new more detailed entries are added in the MeSH thesaurus over time.
Lessons learned from the early performance evaluation of Intel Optane DC Persistent Memory in DBMS
Venue: arXiv cs.DB 2020
Year: 2020

Non-volatile memory (NVM) is an emerging technology, which has the persistence characteristics of large capacity storage devices(e.g., HDDs and SSDs), while providing the low access latency and byte-addressablity of traditional DRAM memory. This unique combination of features open up several new design considerations when building database management systems (DBMSs), such as replacing DRAM (as the main working space memory) or block devices (as the persistent storage), or complementing both at the same time for several DBMS components (such as access methods,storage engine, buffer management, logging/recovery, etc). However, interacting with NVM requires changes to application software to best use the device (e.g. mmap and clflush of small cache lines instead of write and fsync of large page buffers). Before introducing (potentially major) code changes to the DBMS for NVM, developers need a clear understanding of NVM performance in various conditions to help make better design choices. In this paper, we provide extensive performance evaluations conducted with a recently released NVM device, Intel Optane DC Persistent Memory (PMem), under different configurations with several micro-benchmark tools. Further, we evaluate OLTP and OLAP database workloads (i.e., TPC-C and TPC-H) with Microsoft SQL Server 2019 when using the NVM device as an in-memory buffer pool or persistent storage. From the lessons learned we share some recommendations for future DBMS design with PMem, e.g.simple hardware or software changes are not enough for the best use of PMem in DBMSs.
Recent Advances in SQL Query Generation: A Survey
Venue: arXiv cs.CL 2020
Year: 2020

Natural language is hypothetically the best user interface for many domains. However, general models that provide an interface between natural language and any other domain still do not exist. Providing natural language interface to relational databases could possibly attract a vast majority of users that are or are not proficient with query languages. With the rise of deep learning techniques, there is extensive ongoing research in designing a suitable natural language interface to relational databases. This survey aims to overview some of the latest methods and models proposed in the area of SQL query generation from natural language. We describe models with various architectures such as convolutional neural networks, recurrent neural networks, pointer networks, reinforcement learning, etc. Several datasets intended to address the problem of SQL query generation are interpreted and briefly overviewed. In the end, evaluation metrics utilized in the field are presented mainly as a combination of execution accuracy and logical form accuracy.
Finding Game Levels with the Right Difficulty in a Few Trials through Intelligent Trial-and-Error
Venue: arXiv cs.AI 2020
Year: 2020

Methods for dynamic difficulty adjustment allow games to be tailored to particular players to maximize their engagement. However, current methods often only modify a limited set of game features such as the difficulty of the opponents, or the availability of resources. Other approaches, such as experience-driven Procedural Content Generation (PCG), can generate complete levels with desired properties such as levels that are neither too hard nor too easy, but require many iterations. This paper presents a method that can generate and search for complete levels with a specific target difficulty in only a few trials. This advance is enabled by through an Intelligent Trial-and-Error algorithm, originally developed to allow robots to adapt quickly. Our algorithm first creates a large variety of different levels that vary across predefined dimensions such as leniency or map coverage. The performance of an AI playing agent on these maps gives a proxy for how difficult the level would be for another AI agent (e.g. one that employs Monte Carlo Tree Search instead of Greedy Tree Search); using this information, a Bayesian Optimization procedure is deployed, updating the difficulty of the prior map to reflect the ability of the agent. The approach can reliably find levels with a specific target difficulty for a variety of planning agents in only a few trials, while maintaining an understanding of their skill landscape.
Keystroke Biometrics in Response to Fake News Propagation in a Global Pandemic
Venue: arXiv cs.CR 2020
Year: 2020

This work proposes and analyzes the use of keystroke biometrics for content de-anonymization. Fake news have become a powerful tool to manipulate public opinion, especially during major events. In particular, the massive spread of fake news during the COVID-19 pandemic has forced governments and companies to fight against missinformation. In this context, the ability to link multiple accounts or profiles that spread such malicious content on the Internet while hiding in anonymity would enable proactive identification and blacklisting. Behavioral biometrics can be powerful tools in this fight. In this work, we have analyzed how the latest advances in keystroke biometric recognition can help to link behavioral typing patterns in experiments involving 100,000 users and more than 1 million typed sequences. Our proposed system is based on Recurrent Neural Networks adapted to the context of content de-anonymization. Assuming the challenge to link the typed content of a target user in a pool of candidate profiles, our results show that keystroke recognition can be used to reduce the list of candidate profiles by more than 90%. In addition, when keystroke is combined with auxiliary data (such as location), our system achieves a Rank-1 identification performance equal to 52.6% and 10.9% for a background candidate list composed of 1K and 100K profiles, respectively.
Semantic Photo Manipulation with a Generative Image Prior
Venue: arXiv cs.CV 2020
Year: 2020

Despite the recent success of GANs in synthesizing images conditioned on inputs such as a user sketch, text, or semantic labels, manipulating the high-level attributes of an existing natural photograph with GANs is challenging for two reasons. First, it is hard for GANs to precisely reproduce an input image. Second, after manipulation, the newly synthesized pixels often do not fit the original image. In this paper, we address these issues by adapting the image prior learned by GANs to image statistics of an individual image. Our method can accurately reconstruct the input image and synthesize new content, consistent with the appearance of the input image. We demonstrate our interactive system on several semantic image editing tasks, including synthesizing new objects consistent with background, removing unwanted objects, and changing the appearance of an object. Quantitative and qualitative comparisons against several existing methods demonstrate the effectiveness of our method.
Face Identity Disentanglement via Latent Space Mapping
Venue: arXiv cs.CV 2020
Year: 2020

Learning disentangled representations of data is a fundamental problem in artificial intelligence. Specifically, disentangled latent representations allow generative models to control and compose the disentangled factors in the synthesis process. Current methods, however, require extensive supervision and training, or instead, noticeably compromise quality. In this paper, we present a method that learns how to represent data in a disentangled way, with minimal supervision, manifested solely using available pre-trained networks. Our key insight is to decouple the processes of disentanglement and synthesis, by employing a leading pre-trained unconditional image generator, such as StyleGAN. By learning to map into its latent space, we leverage both its state-of-the-art quality, and its rich and expressive latent space, without the burden of training it. We demonstrate our approach on the complex and high dimensional domain of human heads. We evaluate our method qualitatively and quantitatively, and exhibit its success with de-identification operations and with temporal identity coherency in image sequences. Through extensive experimentation, we show that our method successfully disentangles identity from other facial attributes, surpassing existing methods, even though they require more training and supervision.
SDN Enabled and OpenFlow Compatible Network Performance Monitoring System
Venue: arXiv cs.NI 2020
Year: 2020

Network performance monitoring holds a pivotal role in improving the overall network performance. It is essential to monitor the traffic statistics at Internet eXchange Points (IXPs) to optimize the traffic flows. The existing monitoring system either lacks usability or programmability. We present a Software Defined Networking (SDN) enabled and OpenFlow (OF) compatible network performance monitoring system (SDX-Manager) to improve the usability or programmability of IXP simultaneously. The proposed system integrates traditional IXP-Manager with the SDN controller and Grafana to better visualize the traffic. It provides a dashboard to the network administrator for defining rules over IXP, such as port blocking and traffic redirection. Furthermore, it facilitates the administrative tasks that include adding, modifying, and deleting the users and IXP objects (Ports, Switches). The proposed SDX-Manager is deployed and tested in a virtual and physical environment using OF enabled Allied Telesis Switch. Results indicate that SDX-Manager has improved network analysis using different statistics such as packets in/out per second, bits in/out per second, scrape duration, and query count.
Efficient Federated Learning over Multiple Access Channel with Differential Privacy Constraints
Venue: arXiv cs.LG 2020
Year: 2020

In this paper, the problem of federated learning (FL) through digital communication between clients and a parameter server (PS) over a multiple access channel (MAC), also subject to differential privacy (DP) constraints, is studied. More precisely, we consider the setting in which clients in a centralized network are prompted to train a machine learning model using their local datasets. The information exchange between the clients and the PS takes places over a MAC channel and must also preserve the DP of the local datasets. Accordingly, the objective of the clients is to minimize the training loss subject to (i) rate constraints for reliable communication over the MAC and (ii) DP constraint over the local datasets. For this optimization scenario, we proposed a novel consensus scheme in which digital distributed stochastic gradient descent (D-DSGD) is performed by each client. To preserve DP, a digital artificial noise is also added by the users to the locally quantized gradients. The performance of the scheme is evaluated in terms of the convergence rate and DP level for a given MAC capacity. The performance is optimized over the choice of the quantization levels and the artificial noise parameters. Numerical evaluations are presented to validate the performance of the proposed scheme.
Quantized Massive MIMO Systems with Multicell Coordinated Beamforming and Power Control
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, we investigate a coordinated multipoint (CoMP) beamforming and power control problem for base stations (BSs) with a massive number of antenna arrays under coarse quantization at low-resolution analog-to-digital converters (ADCs) and digital-to-analog converter (DACs). Unlike high-resolution ADC and DAC systems, non-negligible quantization noise that needs to be considered in CoMP design makes the problem more challenging. We first formulate total power minimization problems of both uplink (UL) and downlink (DL) systems subject to signal-to-interference-and-noise ratio (SINR) constraints. We then derive strong duality for the UL and DL problems under coarse quantization systems. Leveraging the duality, we propose a framework that is directed toward a twofold aim: to discover the optimal transmit powers in UL by developing iterative algorithm in a distributed manner and to obtain the optimal precoder in DL as a scaled instance of UL combiner. Under homogeneous transmit power and SINR constraints per cell, we further derive a deterministic solution for the UL CoMP problem by analyzing the lower bound of the SINR. Lastly, we extend the derived result to wideband orthogonal frequency-division multiplexing systems to optimize transmit power and beamformer for all subcarriers. Simulation results validate the theoretical results and proposed algorithms.
Tight Analysis of Asynchronous Rumor Spreading in Dynamic Networks
Venue: arXiv cs.DS 2020
Year: 2020

The asynchronous rumor algorithm spreading propagates a piece of information, the so-called rumor, in a network. Starting with a single informed node, each node is associated with an exponential time clock with rate $1$ and calls a random neighbor in order to possibly exchange the rumor. Spread time is the first time when all nodes of a network are informed with high probability. We consider spread time of the algorithm in any dynamic evolving network, $\mathcal{G}=\{G^{(t)}\}_{t=0}^{\infty}$, which is a sequence of graphs exposed at discrete time step $t=0,1\ldots$. We observe that besides the expansion profile of a dynamic network, the degree distribution of nodes over time effect the spread time. We establish upper bounds for the spread time in terms of graph conductance and diligence. For a given connected simple graph $G=(V,E)$, the diligence of cut set $E(S, \overline{S})$ is defined as $ρ(S)=\min_{\{u,v\}\in E(S,\overline{S})}\max\{\bar{d}/d_u, \bar{d}/d_v\}$ where $d_u$ is the degree of $u$ and $\bar{d}$ is the average degree of nodes in the one side of the cut with smaller volume (i.e., ${\mathtt{vol}}{(S)}=\sum_{u\in S}d_u$). The diligence of $G$ is also defined as $ρ(G)=\min_{ \emptyset\neq S\subset V}ρ(S)$. We show that the spread time of the algorithm in $\mathcal{G}$ is bounded by $T$, where $T$ is the first time that $\sum_{t=0}^TΦ(G^{(t)})\cdotρ(G^{(t)})$ exceeds $C\log n$, where $Φ(G^{(t)})$ denotes the conductance of $G^{(t)}$ and $C$ is a specified constant. We also define the absolute diligence as $\overlineρ(G)=\min_{\{u,v\}\in E}\max\{1/d_u,1/d_v\}$ and establish upper bound $T$ for the spread time in terms of absolute diligence, which is the first time when $\sum_{t=0}^T\lceilΦ(G^{(t)})\rceil\cdot \overlineρ(G^{(t)})\ge 2n$. We present dynamic networks where the given upper bounds are almost tight.